Brain Activity Tracking During Electronic Gaming

ABSTRACT

An illustrative system includes a brain interface system configured to be worn by a user and to output brain activity data representative of brain activity of the user while the user concurrently plays an electronic game and a computing device configured to obtain the brain activity data and modify, based on the brain activity data, an attribute of the electronic game.

RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 63/234,113, filed on Aug. 17, 2021,and to U.S. Provisional Patent Application No. 63/154,157, filed on Feb.26, 2021, which is incorporated herein by reference in its entirety.

BACKGROUND INFORMATION

Electronic games (e.g., online games, games provided by way ofelectronic gaming devices, etc.) are a source of entertainment, therapy,relaxation, and even income for many users. Gaming can affect a user'sbrain in a variety of ways. For example, a user's brain may be fatigued,stimulated, engaged, and/or otherwise affected depending on a difficultylevel of an electronic game, a content of the electronic game, an amountof time spent playing the electronic game, and/or various other aspectsof the electronic game. As such, real-time insight into how anelectronic game affects a user's brain while the user plays theelectronic game may result in an improved gaming experience for theuser.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various embodiments and are a partof the specification. The illustrated embodiments are merely examplesand do not limit the scope of the disclosure. Throughout the drawings,identical or similar reference numbers designate identical or similarelements.

FIG. 1 shows an exemplary gaming monitoring system.

FIGS. 24, 5A and 5B show various optical measurement systems that mayimplement the brain interface system shown in FIG. 1.

FIGS. 6-7 show various multimodal measurement systems that may implementthe brain interface system shown in FIG. 1.

FIG. 8 shows an exemplary magnetic field measurement system that mayimplement the brain interface system shown in FIG. 1.

FIG. 9 shows an exemplary configuration in which a gaming systempresents an electronic game to a user and outputs game data associatedwith the electronic game.

FIG. 10 shows an exemplary configuration in which a sensor outputssensor data associated with the user.

FIG. 11 shows an illustrative configuration in which a computing deviceis configured to implement a machine learning model to perform one ormore operations based on the brain activity data output by braininterface system.

FIG. 12 shows an exemplary graphical user interface.

FIGS. 13-14 show implementations of brain activity content.

FIG. 15 illustrates an exemplary method.

FIG. 16 illustrates an exemplary computing device.

DETAILED DESCRIPTION

Tracking of a user's brain activity while the user concurrently plays anelectronic game is described herein. For example, an illustrative systemmay include a brain interface system and a computing device. The braininterface system may be configured to be worn by a user and to outputbrain activity data representative of brain activity of the user whilethe user concurrently plays an electronic game. The computing device maybe configured to obtain the brain activity data and modify, based on thebrain activity data, an attribute of the electronic game.

By tracking the user's brain activity while the user concurrently playsan electronic game, various benefits may be realized for the user and/orothers. For example, the tracked brain activity may be used to estimatea cognitive load of the user while the user plays an electronic game,infer a cognitive strategy used by the user while the user plays theelectronic game, determine when the user should take a break from theelectronic game, and/or monitor performance-related brain activity, suchas learning and/or error perception. Additionally or alternatively,based on the user's brain activity, the electronic game may be adjusted(e.g., in substantially real time as the user plays the electronic game)to more effectively achieve a desired effect on the user's brain, suchas by helping the user realize a desired mental state or emotionalresponse. These and other benefits are described more fully herein.

FIG. 1 shows an exemplary gaming monitoring system 100. As shown, gamingmonitoring system 100 includes a brain interface system 102 and acomputing device 106.

Brain interface system 102 is configured to be worn by a user and tooutput brain activity data representative of brain activity of the userwhile the user plays an electronic game.

As used herein, an electronic game may refer to any computer-based gamethat may be presented to the user by way of one or more devices. Forexample, the electronic game may be an internet-based game (e.g., anonline single-player or multi-player game), a game presented by a gamingsystem (e.g., a gaming console, a mobile device, a personal computer,etc.), and/or any other game as may serve a particular implementation.In some examples, computing device 106 itself may present the electronicgame to the user. Alternatively, a gaming system different thancomputing device 106 may present the electronic game to the user. Insome examples, the electronic game requires physical interaction by theuser with one or more user input devices (e.g., a keyboard, a joystick,a mouse, a controller, etc.) for the user to play the electronic game.

As described herein, the brain activity data may include any data outputby any of the implementations of brain interface system 102 describedherein. For example, the brain activity data may include or be based onoptical-based, electrical-based, and/or magnetic field-basedmeasurements of activity within the brain, as described herein.

In some examples, the brain activity data may indicate how well the useris able to function mentally while playing the electronic game. Forexample, the brain activity data may indicate how well the user is ableto focus on certain tasks within the electronic game, how well the useris able to exercise impulse control when playing the electronic game,what the user's mental state is while the user plays the electronic game(e.g., how stressed, anxious, sleep deprived, and/or calmness of theuser), how well the user cooperates or otherwise gets along with others(e.g., other gamers) while playing the electronic game, etc. In someexamples, one or more of these measures may be represented by a singlebrain activity score that is derived from the brain activity data. Thissingle brain activity score may be generated in any suitable manner.

The measured brain activity could be related to physiological brainstates and/or mental brain states, e.g., joy, excitement, relaxation,surprise, fear, stress, anxiety, sadness, anger, disgust, contempt,contentment, calmness, approval, focus, attention, creativity, cognitiveassessment, positive or negative reflections/attitude on experiences orthe use of objects, etc. Further details on the methods and systemsrelated to a predicted brain state, behavior, preferences, or attitudeof the user, and the creation, training, and use of neuromes can befound in U.S. patent application Ser. No. 17/188,298, filed Mar. 1,2021, issued as U.S. Pat. No. 11,132,625. Exemplary measurement systemsand methods using biofeedback for awareness and modulation of mentalstate are described in more detail in U.S. patent application Ser. No.16/364,338, filed Mar. 26, 2019, issued as U.S. Pat. No. 11,006,876.Exemplary measurement systems and methods used for detecting andmodulating the mental state of a user using entertainment selections,e.g., music, film/video, are described in more detail in U.S. patentapplication Ser. No. 16/835,972, filed Mar. 31, 2020, issued as U.S.Pat. No. 11,006,878. Exemplary measurement systems and methods used fordetecting and modulating the mental state of a user using productformulation from, e.g., beverages, food, selective food/drinkingredients, fragrances, and assessment based on product-elicited brainstate measurements are described in more detail in U.S. patentapplication Ser. No. 16/853,614, filed Apr. 20, 2020, issued as U.S.Pat. No. 11,172,869. Exemplary measurement systems and methods used fordetecting and modulating the mental state of a user through awareness ofpriming effects are described in more detail in U.S. patent applicationSer. No. 16/885,596, filed May 28, 2020, published as US2020/0390358A1.Exemplary measurement systems and methods used for wellness therapy,such as pain management regime, are described more fully in U.S.Provisional Application No. 63/188,783, filed May 14, 2021. Theseapplications and corresponding U.S. patents and publications areincorporated herein by reference in their entirety.

Computing device 106 is configured to obtain (e.g., receive or otherwiseaccess) the brain activity data and, based on the brain activity data,modify an attribute of the electronic game. To this end, computingdevice 106 may generate game control data based on the brain activitydata. The game control data may be used to modify the attribute of theelectronic game.

As used herein, an attribute of an electronic game that may be modifiedmay include any customizable feature of the electronic game. Forexample, the attribute may refer to a difficulty level of the electronicgame, content that is presented to the user while the user plays theelectronic game, a storyline of the electronic game, a duration of theelectronic game, a volume of the electronic game, a color scheme used inthe electronic game, etc.

In some examples, brain interface system 102 may output the brainactivity data in substantially real time while the user plays theelectronic game, or concurrently while the user plays the electronicgame. Furthermore, computing device 106 may obtain the brain activitydata and modify the attribute of the electronic game in substantiallyreal time while the user plays the electronic game, or concurrentlywhile the user plays the electronic game. As used herein, “real time”and “substantially real time” and “concurrently” will be understood torelate to data processing and/or other actions that are performedimmediately, as well as conditions and/or circumstances that areaccounted for as they exist in the moment, or at the same time, when theprocessing or other actions are performed. For example, a real-timeoperation may refer to an operation that is performed immediately andwithout undue delay, even if it is not possible for there to beabsolutely zero delay. Similarly, real-time data, real-timerepresentations, real-time conditions, at the same time conditions, andso forth, will be understood to refer to data, representations, andconditions that relate to a present moment in time or a moment in timewhen decisions are being made and operations are being performed (e.g.,even if after a short delay), such that the data, representations,conditions, and so forth are temporally relevant to the decisions beingmade and/or the operations being performed.

Computing device 106 may be implemented by one or more computingdevices, such as one or more personal computers, mobile devices (e.g., amobile phone, a tablet computer, etc.), servers, and/or any other typeof computing device as may serve a particular implementation.

As shown, computing device 106 may include memory 108 and a processor110. Computing device 106 may include additional or alternativecomponents as may serve a particular implementation. Each component maybe implemented by any suitable combination of hardware and/or software.

Memory 108 may maintain (e.g., store) executable data used by processor110 to perform one or more of the operations described herein as beingperformed by computing device 106. For example, memory 108 may storeinstructions 112 that may be executed by processor 110 to generate gamecontrol data and/or perform one or more operations based on the gamecontrol data. Instructions 112 may be implemented by any suitableapplication, program, software, code, and/or other executable datainstance. Memory 108 may also maintain any data received, generated,managed, used, and/or transmitted by processor 110.

Processor 110 may be configured to perform (e.g., execute instructions112 stored in memory 108 to perform) various operations described hereinas being performed by computing device 106. Examples of such operationsare described herein.

Brain interface system 102 may be implemented by any suitable wearablenon-invasive brain interface system as may serve a particularimplementation. For example, brain interface system 102 may beimplemented by a wearable optical measurement system configured toperform optical-based brain data acquisition operations, such as any ofthe wearable optical measurement systems described in U.S. patentapplication Ser. No. 17/176,315, filed Feb. 16, 2021 and published asUS2021/0259638A1; U.S. patent application Ser. No. 17/176,309, filedFeb. 16, 2021 and published as US2021/0259614A1; U.S. patent applicationSer. No. 17/176,460, filed Feb. 16, 2021 and issued as U.S. Pat. No.11,096,620; U.S. patent application Ser. No. 17/176,470, filed Feb. 16,2021 and published as US2021/0259619A1; U.S. patent application Ser. No.17/176,487, filed Feb. 16, 2021 and published as US2021/0259632A1; U.S.patent application Ser. No. 17/176,539, filed Feb. 16, 2021 andpublished as US2021/0259620A1; U.S. patent application Ser. No.17/176,560, filed Feb. 16, 2021 and published as US2021/0259597A1; U.S.patent application Ser. No. 17/176,466, filed Feb. 16, 2021 andpublished as US2021/0263320A1; Han Y. Ban, et al., “Kernel Flow: A HighChannel Count Scalable TD-fNIRS System,” SPIE Photonics West Conference(Mar. 6, 2021); and Han Y. Ban, et al., “Kernel Flow: a high channelcount scalable time-domain functional near-infrared spectroscopysystem,” Journal of Biomedical Optics (Jan. 18, 2022), whichapplications and publications are incorporated herein by reference intheir entirety.

To illustrate, FIGS. 2-4, 5A, and 5B, show various optical measurementsystems and related components that may implement brain interface system102. The optical measurement systems described herein are merelyillustrative of the many different optical-based brain interface systemsthat may be used in accordance with the systems and methods describedherein.

FIG. 2 shows an optical measurement system 200 that may be configured toperform an optical measurement operation with respect to a body 202(e.g., the brain).

Optical measurement system 200 may, in some examples, be portable and/orwearable by a user.

In some examples, optical measurement operations performed by opticalmeasurement system 200 are associated with a time domain-based opticalmeasurement technique. Example time domain-based optical measurementtechniques include, but are not limited to, time-correlatedsingle-photon counting (TCSPC), time domain near infrared spectroscopy(TD-NIRS), time domain diffusive correlation spectroscopy (TD-DCS), andtime domain digital optical tomography (TD-DOT).

Optical measurement system 200 (e.g., an optical measurement system thatis implemented by a wearable device or other configuration, and thatemploys a time domain-based (e.g., TD-NIRS) measurement technique) maydetect blood oxygenation levels and/or blood volume levels by measuringthe change in shape of laser pulses after they have passed throughtarget tissue, e.g., brain, muscle, finger, etc. As used herein, a shapeof laser pulses refers to a temporal shape, as represented for exampleby a histogram generated by a time-to-digital converter (TDC) coupled toan output of a photodetector, as will be described more fully below.

As shown, optical measurement system 200 includes a detector 204 thatincludes a plurality of individual photodetectors (e.g., photodetector206), a processor 208 coupled to detector 204, a light source 210, acontroller 212, and optical conduits 214 and 216 (e.g., light pipes).However, one or more of these components may not, in certainembodiments, be considered to be a part of optical measurement system200. For example, in implementations where optical measurement system200 is wearable by a user, processor 208 and/or controller 212 may insome embodiments be separate from optical measurement system 200 and notconfigured to be worn by the user.

Detector 204 may include any number of photodetectors 206 as may serve aparticular implementation, such as 2^(n) photodetectors (e.g., 256, 512,. . . , 26384, etc.), where n is an integer greater than or equal to one(e.g., 4, 5, 8, 20, 21, 24, etc.). Photodetectors 206 may be arranged inany suitable manner.

Photodetectors 206 may each be implemented by any suitable circuitconfigured to detect individual photons of light incident uponphotodetectors 206. For example, each photodetector 206 may beimplemented by a single photon avalanche diode (SPAD) circuit and/orother circuitry as may serve a particular implementation. The SPADcircuit may be gated in any suitable manner or be configured to operatein a free running mode with passive quenching. For example,photodetectors 206 may be configured to operate in a free-running modesuch that photodetectors 206 are not actively armed and disarmed (e.g.,at the end of each predetermined gated time window). In contrast, whileoperating in the free-running mode, photodetectors 206 may be configuredto reset within a configurable time period after an occurrence of aphoton detection event (i.e., after photodetector 206 detects a photon)and immediately begin detecting new photons. However, only photonsdetected within a desired time window (e.g., during each gated timewindow) may be included in the histogram that represents a light pulseresponse of the target (e.g., a temporal point spread function (TPSF)).The terms histogram and TPSF are used interchangeably herein to refer toa light pulse response of a target.

Processor 208 may be implemented by one or more physical processing(e.g., computing) devices. In some examples, processor 208 may executeinstructions (e.g., software) configured to perform one or more of theoperations described herein.

Light source 210 may be implemented by any suitable component configuredto generate and emit light. For example, light source 210 may beimplemented by one or more laser diodes, distributed feedback (DFB)lasers, super luminescent diodes (SLDs), light emitting diodes (LEDs),diode-pumped solid-state (DPSS) lasers, super luminescent light emittingdiodes (sLEDs), vertical-cavity surface-emitting lasers (VCSELs),titanium sapphire lasers, micro light emitting diodes (mLEDs), and/orany other suitable laser or light source. In some examples, the lightemitted by light source 210 is high coherence light (e.g., light thathas a coherence length of at least 5 centimeters) at a predeterminedcenter wavelength.

Light source 210 is controlled by controller 212, which may beimplemented by any suitable computing device (e.g., processor 208),integrated circuit, and/or combination of hardware and/or software asmay serve a particular implementation. In some examples, controller 212is configured to control light source 210 by turning light source 210 onand off and/or setting an intensity of light generated by light source210. Controller 212 may be manually operated by a user, or may beprogrammed to control light source 210 automatically.

Light emitted by light source 210 may travel via an optical conduit 214(e.g., a light pipe, a single-mode optical fiber, and/or or a multi-modeoptical fiber) to body 202 of a subject. Body 202 may include anysuitable turbid medium. For example, in some implementations, body 202is a brain or any other body part of a human or other animal.Alternatively, body 202 may be a non-living object. For illustrativepurposes, it will be assumed in the examples provided herein that body202 is a human brain.

As indicated by arrow 220, the light emitted by light source 210 entersbody 202 at a first location 222 on body 202. Accordingly, a distal endof optical conduit 214 may be positioned at (e.g., right above, inphysical contact with, or physically attached to) first location 222(e.g., to a scalp of the subject). In some examples, the light mayemerge from optical conduit 214 and spread out to a certain spot size onbody 202 to fall under a predetermined safety limit. At least a portionof the light indicated by arrow 220 may be scattered within body 202.

As used herein, “distal” means nearer, along the optical path of thelight emitted by light source 210 or the light received by detector 204,to the target (e.g., within body 202) than to light source 210 ordetector 204. Thus, the distal end of optical conduit 214 is nearer tobody 202 than to light source 210, and the distal end of optical conduit216 is nearer to body 202 than to detector 204. Additionally, as usedherein, “proximal” means nearer, along the optical path of the lightemitted by light source 210 or the light received by detector 204, tolight source 210 or detector 204 than to body 202. Thus, the proximalend of optical conduit 214 is nearer to light source 210 than to body202, and the proximal end of optical conduit 216 is nearer to detector204 than to body 202.

As shown, the distal end of optical conduit 216 (e.g., a light pipe, alight guide, a waveguide, a single-mode optical fiber, and/or amulti-mode optical fiber) is positioned at (e.g., right above, inphysical contact with, or physically attached to) output location 226 onbody 202. In this manner, optical conduit 216 may collect at least aportion of the scattered light (indicated as light 224) as it exits body202 at location 226 and carry light 224 to detector 204. Light 224 maypass through one or more lenses and/or other optical elements (notshown) that direct light 224 onto each of the photodetectors 206included in detector 204. In cases where optical conduit 216 isimplemented by a light guide, the light guide may be spring loadedand/or have a cantilever mechanism to allow for conformably pressing thelight guide firmly against body 202.

Photodetectors 206 may be connected in parallel in detector 204. Anoutput of each of photodetectors 206 may be accumulated to generate anaccumulated output of detector 204. Processor 208 may receive theaccumulated output and determine, based on the accumulated output, atemporal distribution of photons detected by photodetectors 206.Processor 208 may then generate, based on the temporal distribution, ahistogram representing a light pulse response of a target (e.g., braintissue, blood flow, etc.) in body 202. Such a histogram is illustrativeof the various types of brain activity measurements that may beperformed by brain interface system 102.

FIG. 3 shows an exemplary optical measurement system 300 in accordancewith the principles described herein. Optical measurement system 300 maybe an implementation of optical measurement system 200 and, as shown,includes a wearable assembly 302, which includes N light sources 304(e.g., light sources 304-1 through 304-N) and M detectors 306 (e.g.,detectors 306-1 through 306-M). Optical measurement system 300 mayinclude any of the other components of optical measurement system 200 asmay serve a particular implementation. N and M may each be any suitablevalue (i.e., there may be any number of light sources 304 and detectors306 included in optical measurement system 300 as may serve a particularimplementation).

Light sources 304 are each configured to emit light (e.g., a sequence oflight pulses) and may be implemented by any of the light sourcesdescribed herein. Detectors 306 may each be configured to detect arrivaltimes for photons of the light emitted by one or more light sources 304after the light is scattered by the target. For example, a detector 306may include a photodetector configured to generate a photodetectoroutput pulse in response to detecting a photon of the light and atime-to-digital converter (TDC) configured to record a timestamp symbolin response to an occurrence of the photodetector output pulse, thetimestamp symbol representative of an arrival time for the photon (i.e.,when the photon is detected by the photodetector).

Wearable assembly 302 may be implemented by any of the wearable devices,modular assemblies, and/or wearable units described herein. For example,wearable assembly 302 may be implemented by a wearable device (e.g.,headgear) configured to be worn on a user's head. Wearable assembly 302may additionally or alternatively be configured to be worn on any otherpart of a user's body.

Optical measurement system 300 may be modular in that one or morecomponents of optical measurement system 300 may be removed, changedout, or otherwise modified as may serve a particular implementation. Assuch, optical measurement system 300 may be configured to conform tothree-dimensional surface geometries, such as a user's head. Exemplarymodular optical measurement systems comprising a plurality of wearablemodules are described in more detail in one or more of the patentapplications incorporated herein by reference.

FIG. 4 shows an illustrative modular assembly 400 that may implementoptical measurement system 300. Modular assembly 400 is illustrative ofthe many different implementations of optical measurement system 300that may be realized in accordance with the principles described herein.

As shown, modular assembly 400 includes a plurality of modules 402(e.g., modules 402-1 through 402-3) physically distinct one fromanother. While three modules 402 are shown to be included in modularassembly 400, in alternative configurations, any number of modules 402(e.g., a single module up to sixteen or more modules) may be included inmodular assembly 400.

Each module 402 includes a light source (e.g., light source 404-1 ofmodule 402-1 and light source 404-2 of module 402-2) and a plurality ofdetectors (e.g., detectors 406-1 through 406-6 of module 402-1). In theparticular implementation shown in FIG. 4, each module 402 includes asingle light source and six detectors. Each light source is labeled “S”and each detector is labeled “D”.

Each light source depicted in FIG. 4 may be implemented by one or morelight sources similar to light source 210 and may be configured to emitlight directed at a target (e.g., the brain).

Each light source depicted in FIG. 4 may be located at a center regionof a surface of the light source's corresponding module. For example,light source 404-1 is located at a center region of a surface 408 ofmodule 402-1. In alternative implementations, a light source of a modulemay be located away from a center region of the module.

Each detector depicted in FIG. 4 may implement or be similar to detector204 and may include a plurality of photodetectors (e.g., SPADs) as wellas other circuitry (e.g., TDCs), and may be configured to detect arrivaltimes for photons of the light emitted by one or more light sourcesafter the light is scattered by the target.

The detectors of a module may be distributed around the light source ofthe module. For example, detectors 406 of module 402-1 are distributedaround light source 404-1 on surface 408 of module 402-1. In thisconfiguration, detectors 406 may be configured to detect photon arrivaltimes for photons included in light pulses emitted by light source404-1. In some examples, one or more detectors 406 may be close enoughto other light sources to detect photon arrival times for photonsincluded in light pulses emitted by the other light sources. Forexample, because detector 406-3 is adjacent to module 402-2, detector406-3 may be configured to detect photon arrival times for photonsincluded in light pulses emitted by light source 404-2 (in addition todetecting photon arrival times for photons included in light pulsesemitted by light source 404-1).

In some examples, the detectors of a module may all be equidistant fromthe light source of the same module. In other words, the spacing betweena light source (i.e., a distal end portion of a light source opticalconduit) and the detectors (i.e., distal end portions of opticalconduits for each detector) are maintained at the same fixed distance oneach module to ensure homogeneous coverage over specific areas and tofacilitate processing of the detected signals. The fixed spacing alsoprovides consistent spatial (lateral and depth) resolution across thetarget area of interest, e.g., brain tissue. Moreover, maintaining aknown distance between the light source, e.g., light emitter, and thedetector allows subsequent processing of the detected signals to inferspatial (e.g., depth localization, inverse modeling) information aboutthe detected signals. Detectors of a module may be alternativelydisposed on the module as may serve a particular implementation.

In some examples, modular assembly 400 can conform to athree-dimensional (3D) surface of the human subject's head, maintaintight contact of the detectors with the human subject's head to preventdetection of ambient light, and maintain uniform and fixed spacingbetween light sources and detectors. The wearable module assemblies mayalso accommodate a large variety of head sizes, from a young child'shead size to an adult head size, and may accommodate a variety of headshapes and underlying cortical morphologies through the conformabilityand scalability of the wearable module assemblies. These exemplarymodular assemblies and systems are described in more detail in U.S.patent application Ser. No. 17/176,460, filed Feb. 16, 2021 and issuedas U.S. Pat. No. 11,096,620, U.S. patent application Ser. No.17/176,470, filed Feb. 16, 2021 and published as US2021/0259619A1, U.S.patent application Ser. No. 17/176,487, filed Feb. 16, 2021 andpublished as US2021/0259632A1, U.S. patent application Ser. No.17/176,539, filed Feb. 16, 2021 and published as US2021/0259620A1, U.S.patent application Ser. No. 17/176,560, filed Feb. 16, 2021 andpublished as US2021/0259597A1, and U.S. patent application Ser. No.17/176,466, filed Feb. 16, 2021 and published as US2021/0263320A1, whichapplications are incorporated herein by reference in their respectiveentireties.

In FIG. 4, modules 402 are shown to be adjacent to and touching oneanother. Modules 402 may alternatively be spaced apart from one another.For example, FIGS. 5A-5B show an exemplary implementation of modularassembly 400 in which modules 402 are configured to be inserted intoindividual slots 502 (e.g., slots 502-1 through 502-3, also referred toas cutouts) of a wearable assembly 504. In particular, FIG. 5A shows theindividual slots 502 of the wearable assembly 504 before modules 402have been inserted into respective slots 502, and FIG. 5B shows wearableassembly 504 with individual modules 402 inserted into respectiveindividual slots 502.

Wearable assembly 504 may implement wearable assembly 302 and may beconfigured as headgear and/or any other type of device configured to beworn by a user.

As shown in FIG. 5A, each slot 502 is surrounded by a wall (e.g., wall506) such that when modules 402 are inserted into their respectiveindividual slots 502, the walls physically separate modules 402 one fromanother. In alternative embodiments, a module (e.g., module 402-1) maybe in at least partial physical contact with a neighboring module (e.g.,module 402-2).

Each of the modules described herein may be inserted into appropriatelyshaped slots or cutouts of a wearable assembly, as described inconnection with FIGS. 5A-5B. However, for ease of explanation, suchwearable assemblies are not shown in the figures.

As shown in FIGS. 4 and 5B, modules 402 may have a hexagonal shape.Modules 402 may alternatively have any other suitable geometry (e.g., inthe shape of a pentagon, octagon, square, rectangular, circular,triangular, free-form, etc.).

As another example, brain interface system 102 may be implemented by awearable multimodal measurement system configured to perform bothoptical-based brain data acquisition operations and electrical-basedbrain data acquisition operations, such as any of the wearablemultimodal measurement systems described in U.S. Patent ApplicationPublication Nos. 2021/0259638 and 2021/0259614, which publications areincorporated herein by reference in their respective entireties.

To illustrate, FIGS. 6-7 show various multimodal measurement systemsthat may implement brain interface system 102. The multimodalmeasurement systems described herein are merely illustrative of the manydifferent multimodal-based brain interface systems that may be used inaccordance with the systems and methods described herein.

FIG. 6 shows an exemplary multimodal measurement system 600 inaccordance with the principles described herein. Multimodal measurementsystem 600 may at least partially implement optical measurement system200 and, as shown, includes a wearable assembly 602 (which is similar towearable assembly 302), which includes N light sources 604 (e.g., lightsources 604-1 through 604-N, which are similar to light sources 304), Mdetectors 606 (e.g., detectors 606-1 through 606-M, which are similar todetectors 306), and X electrodes (e.g., electrodes 608-1 through 608-X).Multimodal measurement system 600 may include any of the othercomponents of optical measurement system 200 as may serve a particularimplementation. N, M, and X may each be any suitable value (i.e., theremay be any number of light sources 604, any number of detectors 606, andany number of electrodes 608 included in multimodal measurement system600 as may serve a particular implementation).

Electrodes 608 may be configured to detect electrical activity within atarget (e.g., the brain). Such electrical activity may includeelectroencephalogram (EEG) activity and/or any other suitable type ofelectrical activity as may serve a particular implementation. In someexamples, electrodes 608 are all conductively coupled to one another tocreate a single channel that may be used to detect electrical activity.Alternatively, at least one electrode included in electrodes 608 isconductively isolated from a remaining number of electrodes included inelectrodes 608 to create at least two channels that may be used todetect electrical activity.

FIG. 7 shows an illustrative modular assembly 700 that may implementmultimodal measurement system 600. As shown, modular assembly 700includes a plurality of modules 702 (e.g., modules 702-1 through 702-3).While three modules 702 are shown to be included in modular assembly700, in alternative configurations, any number of modules 702 (e.g., asingle module up to sixteen or more modules) may be included in modularassembly 700. Moreover, while each module 702 has a hexagonal shape,modules 702 may alternatively have any other suitable geometry (e.g., inthe shape of a pentagon, octagon, square, rectangular, circular,triangular, free-form, etc.).

Each module 702 includes a light source (e.g., light source 704-1 ofmodule 702-1 and light source 704-2 of module 702-2) and a plurality ofdetectors (e.g., detectors 706-1 through 706-6 of module 702-1). In theparticular implementation shown in FIG. 7, each module 702 includes asingle light source and six detectors. Alternatively, each module 702may have any other number of light sources (e.g., two light sources) andany other number of detectors. The various components of modularassembly 700 shown in FIG. 7 are similar to those described inconnection with FIG. 4.

As shown, modular assembly 700 further includes a plurality ofelectrodes 710 (e.g., electrodes 710-1 through 710-3), which mayimplement electrodes 608. Electrodes 710 may be located at any suitablelocation that allows electrodes 710 to be in physical contact with asurface (e.g., the scalp and/or skin) of a body of a user. For example,in modular assembly 700, each electrode 710 is on a module surfaceconfigured to face a surface of a user's body when modular assembly 700is worn by the user. To illustrate, electrode 710-1 is on surface 708 ofmodule 702-1. Moreover, in modular assembly 700, electrodes 710 arelocated in a center region of each module 702 and surround each module'slight source 704. Alternative locations and configurations forelectrodes 710 are possible.

As another example, brain interface system 102 may be implemented by awearable magnetic field measurement system configured to performmagnetic field-based brain data acquisition operations, such as any ofthe magnetic field measurement systems described in U.S. patentapplication Ser. No. 16/862,879, filed Apr. 30, 2020 and published asUS20200348368A1; U.S. Provisional Application No. 63/170,892, filed Apr.5, 2021, U.S. patent application Ser. No. 17/338,429, filed Jun. 3,2021, and Ethan J. Pratt, et al., “Kernel Flux: A Whole-Head432-Magnetometer Optically-Pumped Magnetoencephalography (OP-MEG) Systemfor Brain Activity Imaging During Natural Human Experiences,” SPIEPhotonics West Conference (Mar. 6, 2021), which applications andpublications are incorporated herein by reference in their entirety. Insome examples, any of the magnetic field measurement systems describedherein may be used in a magnetically shielded environment which allowsfor natural user movement as described for example in U.S. ProvisionalPatent Application No. 63/076,015, filed Sep. 9, 2020, and U.S. patentapplication Ser. No. 17/328,235, filed May 24, 2021 and published asUS2021/0369166A1, which applications are incorporated herein byreference in their entirety.

FIG. 8 shows an exemplary magnetic field measurement system 800 (“system800”) that may implement brain interface system 102. As shown, system800 includes a wearable sensor unit 802 and a controller 804. Wearablesensor unit 802 includes a plurality of magnetometers 806-1 through806-N (collectively “magnetometers 806”, also referred to as opticallypumped magnetometer (OPM) modular assemblies as described below) and amagnetic field generator 808. Wearable sensor unit 802 may includeadditional components (e.g., one or more magnetic field sensors,position sensors, orientation sensors, accelerometers, image recorders,detectors, etc.) as may serve a particular implementation. System 800may be used in magnetoencephalography (MEG) and/or any other applicationthat measures relatively weak magnetic fields.

Wearable sensor unit 802 is configured to be worn by a user (e.g., on ahead of the user). In some examples, wearable sensor unit 802 isportable. In other words, wearable sensor unit 802 may be small andlight enough to be easily carried by a user and/or worn by the userwhile the user moves around and/or otherwise performs daily activities,or may be worn in a magnetically shielded environment which allows fornatural user movement as described more fully in U.S. Provisional PatentApplication No. 63/076,015, and U.S. patent application Ser. No.17/328,235, filed May 24, 2021 and published as US2021/0369166A1,previously incorporated by reference.

Any suitable number of magnetometers 806 may be included in wearablesensor unit 802. For example, wearable sensor unit 802 may include anarray of nine, sixteen, twenty-five, or any other suitable plurality ofmagnetometers 806 as may serve a particular implementation.

Magnetometers 806 may each be implemented by any suitable combination ofcomponents configured to be sensitive enough to detect a relatively weakmagnetic field (e.g., magnetic fields that come from the brain). Forexample, each magnetometer may include a light source, a vapor cell suchas an alkali metal vapor cell (the terms “cell”, “gas cell”, “vaporcell”, and “vapor gas cell” are used interchangeably herein), a heaterfor the vapor cell, and a photodetector (e.g., a signal photodiode).Examples of suitable light sources include, but are not limited to, adiode laser (such as a vertical-cavity surface-emitting laser (VCSEL),distributed Bragg reflector laser (DBR), or distributed feedback laser(DFB)), light-emitting diode (LED), lamp, or any other suitable lightsource. In some embodiments, the light source may include two lightsources: a pump light source and a probe light source.

Magnetic field generator 808 may be implemented by one or morecomponents configured to generate one or more compensation magneticfields that actively shield magnetometers 806 (including respectivevapor cells) from ambient background magnetic fields (e.g., the Earth'smagnetic field, magnetic fields generated by nearby magnetic objectssuch as passing vehicles, electrical devices and/or other fieldgenerators within an environment of magnetometers 806, and/or magneticfields generated by other external sources). For example, magnetic fieldgenerator 808 may include one or more coils configured to generatecompensation magnetic fields in the Z direction, X direction, and/or Ydirection (all directions are with respect to one or more planes withinwhich the magnetic field generator 808 is located). The compensationmagnetic fields are configured to cancel out, or substantially reduce,ambient background magnetic fields in a magnetic field sensing regionwith minimal spatial variability.

Controller 804 is configured to interface with (e.g., control anoperation of, receive signals from, etc.) magnetometers 806 and themagnetic field generator 808. Controller 804 may also interface withother components that may be included in wearable sensor unit 802.

In some examples, controller 804 is referred to herein as a “single”controller 804. This means that only one controller is used to interfacewith all of the components of wearable sensor unit 802. For example,controller 804 may be the only controller that interfaces withmagnetometers 806 and magnetic field generator 808. It will berecognized, however, that any number of controllers may interface withcomponents of magnetic field measurement system 800 as may suit aparticular implementation.

As shown, controller 804 may be communicatively coupled to each ofmagnetometers 806 and magnetic field generator 808. For example, FIG. 8shows that controller 804 is communicatively coupled to magnetometer806-1 by way of communication link 810-1, to magnetometer 806-2 by wayof communication link 810-2, to magnetometer 806-N by way ofcommunication link 810-N, and to magnetic field generator 808 by way ofcommunication link 812. In this configuration, controller 804 mayinterface with magnetometers 806 by way of communication links 810-1through 810-N (collectively “communication links 810”) and with magneticfield generator 808 by way of communication link 812.

Communication links 810 and communication link 812 may be implemented byany suitable wired connection as may serve a particular implementation.For example, communication links 810 may be implemented by one or moretwisted pair cables while communication link 812 may be implemented byone or more coaxial cables. Alternatively, communication links 810 andcommunication link 812 may both be implemented by one or more twistedpair cables. In some examples, the twisted pair cables may beunshielded.

Controller 804 may be implemented in any suitable manner. For example,controller 804 may be implemented by a field-programmable gate array(FPGA), an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a microcontroller, and/or other suitable circuittogether with various control circuitry.

In some examples, controller 804 is implemented on one or more printedcircuit boards (PCBs) included in a single housing. In cases wherecontroller 804 is implemented on a PCB, the PCB may include variousconnection interfaces configured to facilitate communication links 810and 812. For example, the PCB may include one or more twisted pair cableconnection interfaces to which one or more twisted pair cables may beconnected (e.g., plugged into) and/or one or more coaxial cableconnection interfaces to which one or more coaxial cables may beconnected (e.g., plugged into).

In some examples, controller 804 may be implemented by or within acomputing device.

In some examples, a wearable magnetic field measurement system mayinclude a plurality of optically pumped magnetometer (OPM) modularassemblies, which OPM modular assemblies are enclosed within a housingsized to fit into a headgear (e.g., brain interface system 102) forplacement on a head of a user (e.g., human subject). The OPM modularassembly is designed to enclose the elements of the OPM optics, vaporcell, and detectors in a compact arrangement that can be positionedclose to the head of the human subject. The headgear may include anadjustment mechanism used for adjusting the headgear to conform with thehuman subject's head. These exemplary OPM modular assemblies and systemsare described in more detail in U.S. Provisional Patent Application No.63/170,892, filed Apr. 5, 2021, and U.S. patent application Ser. No.17/338,429, filed Jun. 3, 2021, previously incorporated by reference.

At least some of the elements of the OPM modular assemblies, systemswhich can employ the OPM modular assemblies, and methods of making andusing the OPM modular assemblies have been disclosed in U.S. PatentApplication Publications Nos. 2020/0072916; 2020/0056263; 2020/0025844;2020/0057116; 2019/0391213; 2020/0088811; 2020/0057115; 2020/0109481;2020/0123416; 2020/0191883; 2020/0241094; 2020/0256929; 2020/0309873;2020/0334559; 2020/0341081; 2020/0381128; 2020/0400763; 2021/0011094;2021/0015385; 2021/0041512; 2021/0041513; 2021/0063510; and 20210139742,and U.S. Provisional Patent Application Ser. Nos. 62/689,696;62/699,596; 62/719,471; 62/719,475; 62/719,928; 62/723,933; 62/732,327;62/732,791; 62/741,777; 62/743,343; 62/747,924; 62/745,144; 62/752,067;62/776,895; 62/781,418; 62/796,958; 62/798,209; 62/798,330; 62/804,539;62/826,045; 62/827,390; 62/836,421; 62/837,574; 62/837,587; 62/842,818;62/855,820; 62/858,636; 62/860,001; 62/865,049; 62/873,694; 62/874,887;62/883,399; 62/883,406; 62/888,858; 62/895,197; 62/896,929; 62/898,461;62/910,248; 62/913,000; 62/926,032; 62/926,043; 62/933,085; 62/960,548;62/971,132; 63/031,469; 63/052,327; 63/076,015; 63/076,880; 63/080,248;63/135,364; 63/136,415; and 63/170,892, all of which are incorporatedherein by reference in their entireties.

In some examples, one or more components of brain interface system 102,FIG. 1, (e.g., one or more computing devices) may be configured to belocated off the head of the user.

In each of the different brain interface system implementationsdescribed herein, the brain activity data may be based on the type ofoperations performed by the different brain interface systemimplementations. For example, if brain interface system 102 isimplemented by an optical measurement system configured to performoptical-based brain data acquisition operations, the brain activity datamay be based on the optical-based brain data acquisition operations. Asanother example, if brain interface system 102 is implemented by amultimodal measurement system configured to perform optical-based braindata acquisition operations and electrical-based brain data acquisitionoperations, the brain activity data may be based on the optical-basedbrain data acquisition operations and the electrical-based brain dataacquisition operations. As another example, if brain interface system102 is implemented by a magnetic field measurement system configured toperform magnetic field-based brain data acquisition operations, thebrain activity data may be based on the magnetic field-based brain dataacquisition operations.

In some examples, computing device 106 may be configured to obtain gamedata representative of one or more characteristics of the electronicgame, and use the game data in combination with the brain activity datato modify the attribute of the electronic game. The one or morecharacteristics represented by the game data may include a currentdifficulty level of the electronic game, a current scene of theelectronic game that the user is experiencing, a score achieved by theuser while playing the electronic game, an attribute of a user profileused by the user to play the electronic game, and/or any othercharacteristic of the electronic game.

To illustrate, FIG. 9 shows an exemplary configuration 900 in which agaming system 902 presents an electronic game to a user and outputs gamedata associated with the electronic game. While FIG. 9 shows the gamedata being output by gaming system 902, the game data may alternativelybe output by any other computing device as may serve a particularimplementation.

As shown, computing device 106 may obtain the game data (e.g., byaccessing the game data in substantially real time as the game data isoutput by gaming system 902). In the example of FIG. 9, the game controldata output by computing device 106 is based on both the brain activitydata and the game data.

As shown, the game control data may be fed back into gaming system 902.In this manner, the game control data may modify one or more attributesof electronic game being presented by gaming system 902.

In some examples, computing device 106 may be configured to obtainsensor data associated with the user from a sensor not included in thebrain interface system and further base the modification of theattribute of the electronic game on the sensor data.

To illustrate, FIG. 10 shows an exemplary configuration 1000 in which asensor 1002 outputs sensor data associated with the user. As shown,computing device 106 may obtain the sensor data (e.g., by receiving orotherwise accessing the sensor data in substantially real time as thesensor data is output by sensor 1002). In the example of FIG. 10, thegame control data output by computing device 106 is based on both thebrain activity data and the sensor data.

Sensor 1002 may be implemented in any suitable manner. For example,sensor 1002 may be implemented by one or more sensors that perform eyetracking, electrodermal activity (EDA)/conductance, pupillometry, heartrate, heart rate variability, and/or pulse oximetry. Additionally oralternatively, sensor 1002 may be implemented by one or more microphonesconfigured to detect ambient sound of the user while the user plays theelectronic game, one or more inertial motion units (IMUs) configured todetect movement by the user while the user plays the electronic game,etc. Output from one or more of these sensors may be fused to provide awholistic view of the user's experience or performance while playing theelectronic game.

In some examples, computing device 106 may be configured to determine,based on the brain activity data and/or any of the other types of datadescribed herein, a current mental state of the user and obtain datarepresentative of a desired mental state of the user. Such mental statesare described herein. Computing device 106 may accordingly modify theattribute of the electronic game by setting the attribute to a valueconfigured to change the current mental state of the user to the desiredmental state of the user.

For example, if the user indicates (e.g., by providing input) that theuser desires to have a non-stressed mental state, a difficulty level ofthe electronic game may be made easier if the user is not doing well andtherefore feeling frustrated. As another example, a difficulty leveland/or any other aspect of the electronic game may be adjusted based onuser engagement or emotional response as indicated by the brain activitydata.

FIG. 11 shows an illustrative configuration 1100 in which computingdevice 106 is configured to implement a machine learning model 1102 toperform one or more operations based on the brain activity data outputby brain interface system 102. Such operations may include any of theoperations described herein (e.g., generating game control data,presenting content, etc.). As another example, computing device 106 maybe configured to use machine learning model 1102 to generate predicteduser action data based on the brain activity data output by braininterface system 102. The predicted user action data may berepresentative of one or more predicted actions that the user may takewhile playing an electronic game. For example, if the brain activitydata indicates that the user is becoming mentally fatigued, thepredicted user action data may indicate that the user will likely takeone or more relatively risky actions during the electronic game thatresult in sub-optimal performance by the user during the electronicgame. The predicted user action data may be presented to the user and/orone or more other people to assist the user in optimizing game playand/or for one or more other reasons as may serve a particularimplementation.

Machine learning model 1102 may be configured to perform any suitablemachine learning heuristic (also referred to as artificial intelligenceheuristic) to input data, which may be in either the time or frequencydomains. Machine learning model 1102 may accordingly be supervisedand/or unsupervised as may serve a particular implementation and may beconfigured to implement one or more decision tree learning algorithms,association rule learning algorithms, artificial neural network learningalgorithms, deep learning algorithms, bitmap algorithms, and/or anyother suitable data analysis technique as may serve a particularimplementation.

In some examples, machine learning model 1102 is implemented by one ormore neural networks, such as one or more deep convolutional neuralnetworks (CNN) using internal memories of its respective kernels(filters), recurrent neural networks (RNN), and/or long/short termmemory neural networks (LSTM). Machine learning model 1102 may bemulti-layer. For example, machine learning model 1102 may be implementedby a neural network that includes an input layer, one or more hiddenlayers, and an output layer. Machine learning model 1102 may be trainedin any suitable manner.

In some examples, computing device 106 may be configured to present, byway of a graphical user interface, content representative of the brainactivity data to the user and/or to other people watching the user playthe electronic game (e.g., via an online platform such as Twitch).

For example, FIG. 12 shows an exemplary graphical user interface 1200configured to display game content 1202 (e.g., one or more graphicsassociated with the electronic game). As shown, brain activity content1204 representative of the brain activity data may also be displayedtogether with the game content 1202. Graphical user interface 1200 maybe presented to the user (e.g., by way of a display included in orconnected to computing device 106) and/or to one or more other users(e.g., by way of a network, such as the Internet).

Brain activity content 1204 may be presented in any suitable form. Forexample, FIG. 13 shows an implementation 1300 of brain activity content1204 in which the brain activity is displayed as a two-dimensional (2D)map. Alternatively, the brain activity may be displayed as aninteractive three-dimensional (3D) visualization. Using either the 2D or3D representation, an observer could explore the different regions ofactivation while the user plays and interacts with the electronic game.

FIG. 14 shows another implementation 1400 of brain activity content 1204in which the brain activity could be quantified by regions of interestwith a graphical representation of the magnitude of signal in eachregion shown, e.g., by a bar. The brain activity may additionally oralternatively be displayed using an abstract representation similar toan audio visualizer.

Additionally or alternatively, brain activity content 1204 may bepresented in the form of a score, a game report, etc. For example, theuser may receive a game report that shows the user's biological systemresponses during gameplay. The report could include brain data only orextend to brain and physiological data if multiple sensors are combined.This report could suggest how a user could improve a score or game playexperience, or highlight times when brain activity was optimized forperformance. The game report might also suggest which parts of the gamea player found enjoyable or stressful. A game report could provide acomparison to other users with similar brain activity patterns orphysiological response patterns, or suggest other games that a playermight find enjoyable based on their brain activity or physiologicalresponses to a game. Game reports could also be compared for the sameperson over time to show improvement or changes in cognitive strategiesduring gaming.

In some examples, the physiological response data can be obtained ifmultiple sensors are combined using other types of optical measurementsystems having form factors that can be worn (e.g., on the finger,wrist, feet, toes, forehead, chest, ear, or any other body part). Suchoptical measurement systems are described more fully in U.S. ProvisionalPatent Application No. 63/134,479, filed Jan. 6, 2021; U.S. ProvisionalPatent Application No. 63/154,116, filed Feb. 26, 2021; U.S. ProvisionalPatent Application No. 63/160,995, filed Mar. 15, 2021; U.S. ProvisionalPatent Application No. 63/179,080, filed Apr. 23, 2021; U.S. ProvisionalPatent Application No. 63/154,127, filed Feb. 26, 2021; U.S. ProvisionalPatent Application No. 63/191,822, filed May 21, 2021; U.S. ProvisionalPatent Application No. 63/154,131, filed Feb. 26, 2021; and U.S.Provisional Patent Application No. 63/196,917, filed Jun. 4, 2021, whichapplications are incorporated herein by reference in their entirety.

In some examples, the physiological response data can be obtained usinga wearable extended reality-based neuroscience analysis system asdescribed in U.S. Provisional Patent Application No. 63/139,469, filedJan. 20, 2021, and U.S. Provisional Patent Application No. 63/139,478,filed Jan. 20, 2021, which applications are incorporated herein byreference in their entirety.

FIG. 15 illustrates an exemplary method 1500. While FIG. 15 illustratesexemplary operations according to one embodiment, other embodiments mayomit, add to, reorder, and/or modify any of the operations shown in FIG.15. One or more of the operations shown in FIG. 15 may be performed bycomputing device 106 and/or any implementation thereof. Each of theoperations illustrated in FIG. 15 may be performed in any suitablemanner.

At operation 1502, a computing device may obtain, from a brain interfacesystem configured to be worn by a user while the user plays anelectronic game, brain activity data representative of brain activity ofthe user while the user concurrently plays the electronic game.

At operation 1504, the computing device may modify, based on the brainactivity data, an attribute of the electronic game.

In some examples, a non-transitory computer-readable medium storingcomputer-readable instructions may be provided in accordance with theprinciples described herein. The instructions, when executed by aprocessor of a computing device, may direct the processor and/orcomputing device to perform one or more operations, including one ormore of the operations described herein. Such instructions may be storedand/or transmitted using any of a variety of known computer-readablemedia.

A non-transitory computer-readable medium as referred to herein mayinclude any non-transitory storage medium that participates in providingdata (e.g., instructions) that may be read and/or executed by acomputing device (e.g., by a processor of a computing device). Forexample, a non-transitory computer-readable medium may include, but isnot limited to, any combination of non-volatile storage media and/orvolatile storage media. Exemplary non-volatile storage media include,but are not limited to, read-only memory, flash memory, a solid-statedrive, a magnetic storage device (e.g. a hard disk, a floppy disk,magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and anoptical disc (e.g., a compact disc, a digital video disc, a Blu-raydisc, etc.). Exemplary volatile storage media include, but are notlimited to, RAM (e.g., dynamic RAM).

FIG. 16 illustrates an exemplary computing device 1600 that may bespecifically configured to perform one or more of the processesdescribed herein. Any of the systems, units, computing devices, and/orother components described herein may be implemented by computing device1600.

As shown in FIG. 16, computing device 1600 may include a communicationinterface 1602, a processor 1604, a storage device 1606, and aninput/output (“I/O”) module 1608 communicatively connected one toanother via a communication infrastructure 1610. While an exemplarycomputing device 1600 is shown in FIG. 16, the components illustrated inFIG. 16 are not intended to be limiting. Additional or alternativecomponents may be used in other embodiments. Components of computingdevice 1600 shown in FIG. 16 will now be described in additional detail.

Communication interface 1602 may be configured to communicate with oneor more computing devices. Examples of communication interface 1602include, without limitation, a wired network interface (such as anetwork interface card), a wireless network interface (such as awireless network interface card), a modem, an audio/video connection,and any other suitable interface.

Processor 1604 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 1604 may performoperations by executing computer-executable instructions 1612 (e.g., anapplication, software, code, and/or other executable data instance)stored in storage device 1606.

Storage device 1606 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 1606 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 1606. For example, data representative ofcomputer-executable instructions 1612 configured to direct processor1604 to perform any of the operations described herein may be storedwithin storage device 1606. In some examples, data may be arranged inone or more databases residing within storage device 1606.

I/O module 1608 may include one or more I/O modules configured toreceive user input and provide user output. I/O module 1608 may includeany hardware, firmware, software, or combination thereof supportive ofinput and output capabilities. For example, I/O module 1608 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 1608 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 1608 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation.

An illustrative system includes a brain interface system configured tobe worn by a user and to output brain activity data representative ofbrain activity of the user while the user plays an electronic game; anda computing device configured to obtain the brain activity data, andmodify, based on the brain activity data, an attribute of the electronicgame.

An illustrative apparatus includes a memory storing instructions and aprocessor communicatively coupled to the memory and configured toexecute the instructions to: obtain, from a brain interface systemconfigured to be worn by a user while the user concurrently plays anelectronic game, brain activity data representative of brain activity ofthe user while the user plays the electronic game, and modify, based onthe brain activity data, an attribute of the electronic game.

An illustrative method includes obtaining, by a computing device from abrain interface system configured to be worn by a user while the userconcurrently plays an electronic game, brain activity datarepresentative of brain activity of the user while the user plays theelectronic game; and modifying, by the computing device based on thebrain activity data, an attribute of the electronic game.

An illustrative non-transitory computer-readable medium storinginstructions that, when executed, direct a processor of a computingdevice to: obtain, from a brain interface system configured to be wornby a user while the user concurrently plays an electronic game, brainactivity data representative of brain activity of the user while theuser plays the electronic game; and modify, based on the brain activitydata, an attribute of the electronic game.

In the preceding description, various exemplary embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe scope of the invention as set forth in the claims that follow. Forexample, certain features of one embodiment described herein may becombined with or substituted for features of another embodimentdescribed herein. The description and drawings are accordingly to beregarded in an illustrative rather than a restrictive sense.

1. A system comprising: a brain interface system configured to be wornby a user and to output brain activity data representative of brainactivity of the user while the user concurrently plays an electronicgame; and a computing device configured to obtain the brain activitydata, and modify, based on the brain activity data, an attribute of theelectronic game.
 2. The system of claim 1, wherein the brain interfacesystem comprises an optical measurement system configured to performoptical-based brain data acquisition operations, the brain activity databased on the optical-based brain data acquisition operations.
 3. Thesystem of claim 2, wherein the optical measurement system comprises: awearable assembly configured to be worn by the user and comprising: aplurality of light sources each configured to emit light directed at abrain of the user, and a plurality of detectors configured to detectarrival times for photons of the light after the light is scattered bythe brain, the brain activity data based on the arrival times.
 4. Thesystem of claim 3, wherein the detectors each comprise a plurality ofsingle-photon avalanche diode (SPAD) circuits.
 5. The system of claim 3,wherein the wearable assembly further comprises: a first modulecomprising a first light source included in the plurality of lightsources and a first set of detectors included in the plurality ofdetectors; and a second module physically distinct from the first moduleand comprising a second light source included in the plurality of lightsources and a second set of detectors included in the plurality ofdetectors.
 6. The system of claim 5, wherein the first and secondmodules are configured to be removably attached to the wearableassembly.
 7. The system of claim 1, wherein the brain interface systemcomprises a multimodal measurement system configured to performoptical-based brain data acquisition operations and electrical-basedbrain data acquisition operations, the brain activity data based on theoptical-based brain data acquisition operations and the electrical-basedbrain data acquisition operations.
 8. The system of claim 7, wherein themultimodal measurement system comprises: a wearable assembly configuredto be worn by the user and comprising: a plurality of light sources eachconfigured to emit light directed at a brain of the user, a plurality ofdetectors configured to detect arrival times for photons of the lightafter the light is scattered by the brain, and a plurality of electrodesconfigured to be external to the user and detect electrical activity ofthe brain, the brain activity based on the arrival times and theelectrical activity.
 9. The system of claim 8, wherein the wearableassembly further comprises: a first module comprising a first lightsource included in the plurality of light sources and a first set ofdetectors included in the plurality of detectors; and a second modulephysically distinct from the first module and comprising a second lightsource included in the plurality of light sources and a second set ofdetectors included in the plurality of detectors.
 10. The system ofclaim 9, wherein the plurality of electrodes comprises a first electrodeon a surface of the first module and a second electrode on a surface ofthe second module.
 11. The system of claim 10, wherein the firstelectrode surrounds the first light source on the surface of the firstmodule.
 12. The system of claim 1, wherein the brain interface systemcomprises a magnetic field measurement system configured to performmagnetic field-based brain data acquisition operations, the brainactivity data based on the magnetic field-based brain data acquisitionoperations.
 13. The system of claim 12, wherein the magnetic fieldmeasurement system comprises a wearable sensor unit configured to beworn by the user and comprising a magnetometer configured to detect amagnetic field generated within a brain of the user.
 14. The system ofclaim 1, wherein the outputting the brain activity data, the obtainingthe brain activity data, and the modifying the attribute of theelectronic game are performed in substantially real time while the userconcurrently plays the electronic game.
 15. The system of claim 1,wherein the electronic game requires physical interaction by the userwith one or more user input devices for the user to play the electronicgame.
 16. The system of claim 1, wherein: the computing device isfurther configured to obtain game data representative of one or morecharacteristics of the electronic game; and the modifying of theattribute of the electronic game is further based on the game data. 17.The system of claim 16, wherein the one or more characteristics of thegame represented by the game data comprise one or more of a currentdifficulty level of the electronic game, a current scene of theelectronic game that the user is experiencing, a score achieved by theuser while playing the electronic game, or an attribute of a userprofile used by the user to play the electronic game.
 18. The system ofclaim 16, wherein the obtaining of the game data comprises receiving thegame data from a gaming system that presents the electronic game to theuser.
 19. The system of claim 1, wherein the computing device is furtherconfigured to present the electronic game to the user.
 20. The system ofclaim 1, wherein: the computing device is further configured to:determine, based on the brain activity data, a current mental state ofthe user, and obtain data representative of a desired mental state ofthe user; wherein the modifying the attribute of the electronic gamecomprises setting the attribute to a value configured to change thecurrent mental state of the user to the desired mental state of theuser.
 21. The system of claim 1, wherein the computing device is furtherconfigured to present, by way of a graphical user interface, contentrepresentative of the brain activity data.
 22. The system of claim 21,wherein the electronic game is presented to the user via the graphicaluser interface.
 23. The system of claim 21, wherein the graphical userinterface is presented to one or more other users by way of a network.24. The system of claim 1, wherein: the computing device is furtherconfigured to obtain sensor data associated with the user from a sensornot included in the brain interface system; and the modifying of theattribute of the electronic game is further based on the sensor data.25. The system of claim 24, wherein the sensor data is representative ofone or more of eye movement by the user, electrodermal activity of theuser, pupillometry associated with the user, a heart rate of the user,heart rate variability of the user, or a pulse oximetry reading for theuser.
 26. The system of claim 1, wherein the computing device is furtherconfigured to predict, based on the brain activity data, an action thatthe user will perform while playing the electronic game. 27-49.(canceled)